[dehrmann]

I think Java got this mostly right. On the threading front, very little is thread-safe or atomic (x += 1 is not thread-safe), so as soon as you expose something to threads, you have to think about safe access. For interacting with C code, your choices are either shared buffers or copying data between C and Java. It's painful, but it's needed for memory safety.

[rogerbinns]

The core Python data structures are atomic to Python developers. eg there is no way you can corrupt a list or dictionary no matter how much concurrency you try to use. This was traditionally done under the protection of the global interpreter lock which ensured that only one piece of C code at a time was operating with the internals of those objects. C code can also release the GIL eg during I/O, or operations in other libraries that aren't interacting with Python objects, allowing concurrency.

The free threaded implementation adds what amounts to individual object locks at the C level (critical sections). This still means developers writing Python code can do whatever they want, and they will not experience corruption or crashes. The base objects have all been updated.

Python is popular because of many extensions written in C, including many in the standard library. Every single piece of that code must be updated to operate correctly in free threaded mode. That is a lot of work and is still in progress in the standard library. But in order to make the free threaded interpreter useful at this point, some have been marked as free thread safe, when that is not the case.

[hunterpayne]

So its the worst of all possible worlds then. It has the poorest performance due to forced locking even when not necessary and if you load a library in another language (C), then you can still get corruptions. If you really care about performance, probably best to avoid Python entirely, even when its compiled like it is in CPython.

PS For extra fun, learn what the LD_PRELOAD environmental variable does and how it can be used to abuse CPython (or other things that dynamically load shared objects).

[rogerbinns]

It is multiple fine grained locking versus a single global lock. The latter lets you do less locking, but only have a single thread of execution at a time. The former requires more locking but allows multiple concurrent threads of execution. There is no free lunch. But hardware has become parallel so something has to be done to take advantage of that. The default Python remains the GIL version.

The locking is all about reading and writing Python objects. It is not applicable to outside things like external libraries. Python objects are implemented in C code, but Python users do not need to know or care about that.

As a Python user you cannot corrupt or crash things by code you write no matter how hard you try with mutation and concurrency. The locking ensures that. Another way of looking at Python is that it is a friendly syntax for calling code written in C, and that is why people use it - the C code can be where all the performance is, while retaining the ergonomic access.

C code has to opt in to free threading - see my response to this comment

https://news.ycombinator.com/item?id=45706331

It is true that more fine grained locking can end up being done than is strictly necessary, but user's code is loaded at runtime, so you don't know in advance what could be omitted. And this is the beginning of the project - things will get better.

Aside: Yes you can use ctypes to crash things, other compiled languages can be used, concurrency is hard

[int_19h]

It depends on how you define "corruption". You can't get a torn read or write, or mess up a collection to the point where attempts to use it will segfault, sure. You can still end up with corrupt data in a sense of not upholding the expected logic invariants, which is to say, it's still corrupt for any practical purpose (and may in turn lead to taking code paths that are not supposed to ever happen etc).

[int_19h]

A library written in another language would have a Python extension module wrapping it, which would still hold the GIL for the duration of the native call (it can be released, but this is opt-in not opt-out), so that is usually not the issue with this arrangement.

The bigger problem is that it teaches people dangerously misguided notions such as "I don't need to synchronize if I work with built-in Python collections". Which, of course, is only true if a single guaranteed-atomic operation on the collection actually corresponds to a single logical atomic operation in your algorithm. What often happens is people start writing code without locks and it works, so they keep doing it until at some point they do something that actually requires locking (like atomic remove from one collection & add to another) without realizing that they have crossed a line.

Interestingly, we've been there before, multiple times even. The original design of Java collections entailed implicit locking on every operation, with the same exact outcome. Then .NET copied that design in its own collections. Both frameworks dropped it pretty fast, though - Java in v1.2 and .NET in v2.0. But, of course, they could do it because the locking was already specific to collections - it wasn't a global lock used for literally every language object, as in Python.

[Demiurge]

> If you really care about performance, probably best to avoid Python entirely

This has been true forever. Nothing more needs to be said. Please, avoid Python.

On the other hand, I’ve never had issues with Python performance, in 20 years of using it, for all the reasons that have been beaten to death.

It’s great that some people want to do some crazy stuff to CPython, but honestly, don’t hold your breath. Please don’t use Python if Python interpreter performance is your top concern.

[AlphaSite]

It’s another step in the right direction. These things take time.

[ReflectedImage]

Arguably, it's a step in the wrong direction. Share memory by communicating is already doable in Python with Pipe() and Queue() and side steps the issue entirely.

[colonCapitalDee]

Is compound assignment atomic in any major language?

[delusional]

Python and Javascript (in the browser) due to their single threaded nature. C++ too as long as you have a std::atomic on the left hand side (since they overload the operator).

[Groxx]

it has been in Python due to the GIL.

[i80and]

It's not atomic even with the GIL, though: another thread can run in between the bytecode's load and increment, right?

The GIL's guarantees didn't extend to this.

[Groxx]

There is NB_INPLACE_ADD... but I'm struggling to find enough details to be truly confident :\ possibly its existence is misleading other people (thus me) to think += is a single operation in bytecode.

Or, on further reading, maybe it applies to anything that implements `_iadd_` in C. Which does not appear to include native longs: https://github.com/python/cpython/blob/main/Objects/longobje...

[arccy]

no... but some languages may disallow simultaneously holding a reference in different execution threads

[hunterpayne]

> x += 1 is not thread-safe

Nit, that's true iff x is a primitive without the volatile modifier. That's not true for a volatile primitive.

[wmanley]

Even with volatile it’s a load and then a store no? It may not be undefined behaviour, but I don’t think it will be atomic.

[diek]

You're correct. If you have:

  public int someField;
 
  public void inc() {
    someField += 1;
  }

that still compiles down to:

  GETFIELD [someField]
  ICONST_1
  IADD
  PUTFIELD [somefield]

whether 'someField' is volatile or not. The volatile just affects the load/store semantics of the GETFIELD/PUTFIELD ops. For atomic increment you have to go through something like AtomicInteger that will internally use an Unsafe instance to ensure it emits a platform-specific atomic increment instruction.